Earth observation big data for climate change research

Earth observation technology has provided highly useful information in global climate change research over the past few decades and greatly promoted its development,especially through providing biological,physical,and chemical parameters on a global scale.Earth observation data has the 4V features(volume,variety,veracity,and velocity) of big data that are suitable for climate change research.Moreover,the large amount of data available from scientific satellites plays an important role.This study reviews the advances of climate change studies based on Earth observation big data and provides examples of case studies that utilize Earth observation big data in climate change research,such as synchronous satelliteeaerialeground observation experiments,which provide extremely large and abundant datasets; Earth observational sensitive factors(e.g.,glaciers,lakes,vegetation,radiation,and urbanization); and global environmental change information and simulation systems.With the era of global environment change dawning,Earth observation big data will underpin the Future Earth program with a huge volume of various types of data and will play an important role in academia and decisionmaking.Inevitably,Earth observation big data will encounter opportunities and challenges brought about by global climate change.

Applying the Distributed Hydrological Model for Tropical Monsoon Basins by Using Earth Observation Data (Case Studies: Kone and Ba River Basins)

Due to the limitation of data sources, the application of Distributed Hydrological Models (DHMs) using earth observation data to research water resources is necessary. In this study, the BTOPMC (Block-wise use of TOPMODEL) model was applied for 2 basins in the tropical monsoon region. This is the first time that the land cover map of the CCI (Climate Change Initiative Land Cover Team) was prepared for input data instead of IGBP (International Geosphere-Biosphere Programme) land cover map as proposed in the demo version of the BTOPMC model. The calibration and validation results showed that the Nash-Sutcliffe coefficients for daily stream discharge were 77.5% and 68.7% at Cung Son station (Ba basin). The Nash-Sutcliffe coefficients for daily stream discharge were 79.4% and 69.0% at Binh Tuong station (Kone basin), respectively. Because of a stop in measuring the discharge at Binh Tuong station in 2007, this model was applied to simulate discharge during the period of 2008-2015. Furthermore, the effect of land cover on discharge at Cung Son station was considered. The annual discharge in 2010 at Cung Son decreased 8 m3/s in the comparison between two scenarios (land cover of 2000 and 2010). According to this result, it is possible to propose a wide application range of the DHMs model to the tropical monsoon river basins using earth observation data.

2012–2014 China's Earth Observation and Earth Science Development

Human beings are now facing global and regional sustainable development challenges.In China, Earth observation data play a fundamental role in Earth system science research. The support given by Earth observation data is required by many studies, including those on Earth's limited natural resources, the rapid development of economic and social needs, global change, extreme events, food security, water resources, sustainable economic and urban development, and emergency response. Application operation systems in many ministries and departments in China have entered a stage of sustainable development, and the State Key Project of High-Resolution Earth Observation Systems has been progressing since 2006. Earth observation technology in China has entered a period of rapid development.

Progress of Earth Observation in China

China is expanding and sharing its capacity for Earth observation by developing sensors,platforms,and launch capabilities in tandem with growing lunar and deep space exploration.China is considering the Moon as a viable Earth observation platform to provide high-quality,planetary-scale data.The platform would produce consistent spatiotemporal data because of its long operational life and the geological stability of the Moon.China is also quickly improving its capabilities in processing and transforming Earth observation data into useful and practical information.Programs such as the Big Earth Data Science Engineering Program(CASEarth)provide opportunities to integrate data and develop“Big Earth Data”platforms to add value to data through analysis and integration.Such programs can offer products and services independently and in collaboration with international partners for data-driven decision support and policy development.With the rapid digital transformation of societies,and consequently increasing demand for big data and associated products,Digital Earth and the Digital Belt and Road Program(DBAR)allow Chinese experts to collaborate with international partners to integrate valuable Earth observation data in regional and global sustainable development.

Progress of Earth Observation in China

Sustainability is the current theme of global development, and for China, it is not only an opportunity but also a challenge. In 2016, the Paris Agreement on climate change was adopted, addressing the need to limit the rise of global temperatures. The United Nations(UN) has set Sustainable Development Goals(SDGs) to transform our world in terms of closely linking human well-being, economic prosperity, and healthy environments. Sustainable development requires the support of spatial information and objective evaluation,and the capability of macroscopic, rapid, accurate Earth observation techniques plays an important role in sustainable development. Recently, Earth observation technologies are developing rapidly in China, where scientists are building coordinated, comprehensive and sustainable Earth observation systems for global monitoring programs. Recent efforts include the Digital Belt and Road Program(DBAR) and comparative studies of the "three poles". This and other researches will provide powerful support for solving problems such as global change and environmental degradation.

Think global,cube local:an Earth Observation Data Cube’s contribution to the Digital Earth vision

The technological landscape for managing big Earth observation(EO)data ranges from global solutions on large cloud infrastructures with web-based access to self-hosted implementations.EO data cubes are a leading technology for facilitating big EO data analysis and can be deployed on different spatial scales:local,national,regional,or global.Several EO data cubes with a geographic focus(“local EO data cubes”)have been implemented.However,their alignment with the Digital Earth(DE)vision and the benefits and trade-offs in creating and maintaining them ought to be further examined.We investigate local EO data cubes from five perspectives(science,business and industry,government and policy,education,communities and citizens)and illustrate four examples covering three continents at different geographic scales(Swiss Data Cube,semantic EO data cube for Austria,DE Africa,Virginia Data Cube).A local EO data cube can benefit many stakeholders and players but requires several technical developments.These developments include enabling local EO data cubes based on public,global,and cloud-native EO data streaming and interoperability between local EO data cubes.We argue that blurring the dichotomy between global and local aligns with the DE vision to access the world’s knowledge and explore information about the planet.

Recent Progress of Earth Observation Satellites in China

Currently,China has 32 Earth observation satellites in orbit.The satellites can provide various data such as optical,multispectral,infrared,and radar.The spatial resolution of China Earth observation satellites ranges from low to medium to high.The satellites possess the capability to observe across multiple spectral bands,under all weather conditions,and at all times.The data of China Earth observation satellites has been widely used in fields such as natural resource detection,environmental monitoring and protection,disaster prevention and reduction,urban planning and mapping,agricultural and forestry surveys,land survey and geological prospecting,and ocean forecasting,achieving huge social benefits.This article introduces the recent progress of Earth observation satellites in China since 2022,especially the satellite operation,data archiving,data distribution and data coverage.

Global geospatial data from Earth observation:status and issues

Data covering the whole of the surface of the Earth in a homogeneous and reliable manner has been accumulating over many years.This type of data became available from meteorological satellites from the 1960s and from Earth-observing satellites at a small scale from the early 1970s but has gradually accumulated at larger scales up to the present day when we now have data covering many environmental themes at large scales.These data have been used to generate information which is presented in the form of global data sets.This paper will give a brief introduction to the development of Earth observation and to the organisations and sensors which collect data and produce global geospatial data sets.Means of accessing global data sets will set out the types of data available that will be covered.Digital elevation models are discussed in a separate section because of their importance in georeferencing image data as well as their application to analysis of thematic data.The paper will also examine issues of availability,accuracy,validation and reliability and will look at future challenges.

Approach and practice: integrating earth observation resources for data sharing in China GEOSS

Earth observation data sharing is an essential part of the data lifecycle and plays a critical role in Earth science research.Existing industry data sharing systems are affected by restrictions in distributed resource management and tightly coupled service interoperability.These systems currently offer no support for facilitating cross-disciplinary exploration and application.The lack of a national data sharing infrastructure has led to reduced international cooperation.These barriers are common and have hindered the development of the Global Earth Observation System of Systems(GEOSS).The China GEOSS Data Sharing Network(China GEOSS DSNet)has been proposed as a part of China’s Plan for Implementing GEOSS(2016–2025)to address the above issues.In this research,we designed a national GEOSS data sharing framework,including resource integration mechanism,sharing-oriented metadata standards,and lightweight interoperability service to coordinate various Earth observation resources.So far,more than 29 million archived satellite metadata records and 200 TB of high-quality satellite datasets have been integrated under this framework.The results were demonstrated in the following applications:domestic satellite archived metadata query service,international Earth observation resource sharing service,and disaster emergency response service.

中国综合地球观测系统的构成与实践研究

针对地球观测领域规模最大的政府间国际组织“地球观测组织(Group on Earth Observations)”提出的“全球综合地球观测系统”这一概念,梳理了其实现和建设的现状,分析了其具有供给导向、元数据质量不高、无法直接支撑决策和行动等局限性。结合我国参加地球观测组织的计划和成果,详细阐述了面向全球服务的中国综合地球观测系统的内涵,并基于全球综合地球观测系统的优势与不足,提出了中国综合地球观测系统平台的系统架构,另外就优质数据集研制、信息专题服务以及数据应急响应3个案例阐述了中国综合地球观测系统平台的实践及成效。

面向省级高分中心遥感影像大数据综合服务平台的设计与实现

为了加快推进高分产业化应用,满足省级高分中心多源海量遥感数据接收、存储、管理、处理、解译和分发等全业务流程一体化与自动化业务需求,本文基于私有云平台及微服务等技术框架,设计并实现了面向省级高分中心一站式接收、一体化标准处理、批量生产、快速分发和多维度展示等全流程遥感影像大数据综合服务平台,提高了省级高分中心日常业务的工作效率,有助于省级大区域至县域高分的推广应用。

Harmonizing models and observations: Data assimilation in Earth system science

Models and observations are two fundamental methodological approaches in Earth system science(ESS). They evolve collaboratively and enhance one another. However, neither of these two approaches is perfect, and they have incompatibilities due to their methodological differences. The emergence of data assimilation(DA) has enabled these two approaches to develop in conjunction and form a harmonic ESS methodology. As a result, DA has shown a fresh vitality and applicability in ESS. This paper reviews the application of DA in the main branches of ESS, traces the coordinated evolution of DA with the methodologies of rationalism and empiricism, analyzes the relationships of DA with estimation theory and cybernetics, summarizes the advances of DA in China, and presents an outlook on the challenges facing the development of a uniform DA for ESS. DA theories and methods will continue to evolve and provide an increasingly mature methodology for enhancing the understanding and prediction of Earth as a system.

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Pressures on natural resources are increasing and a number of challenges need to be overcome to meet the needs of a growing population in a period of environmental variability.Some of these environmental issues can be monitored using remotely sensed Earth Observations(EO)data that are increasingly available from a number of freely and openly accessible repositories.However,the full information potential of EO data has not been yet realized.They remain still underutilized mainly because of their complexity,increasing volume,and the lack of efficient processing capabilities.EO Data Cubes(DC)are a new paradigm aiming to realize the full potential of EO data by lowering the barriers caused by these Big data challenges and providing access to large spatio-temporal data in an analysis ready form.Systematic and regular provision of Analysis Ready Data(ARD)will significantly reduce the burden on EO data users.Nevertheless,ARD are not commonly produced by data providers and therefore getting uniform and consistent ARD remains a challenging task.This paper presents an approach to enable rapid data access and pre-processing to generate ARD using interoperable services chains.The approach has been tested and validated generating Landsat ARD while building the Swiss Data Cube.

Reactive scheduling of multiple EOSs under cloud uncertainties:model and algorithms

Most earth observation satellites(EOSs)are low-orbit satellites equipped with optical sensors that cannot see through clouds.Hence,cloud coverage,high dynamics,and cloud uncertainties are important issues in the scheduling of EOSs.The proactive-reactive scheduling framework has been proven to be effective and efficient for the uncertain scheduling problem and has been extensively employed.Numerous studies have been conducted on methods for the proactive scheduling of EOSs,including expectation,chance-constrained,and robust optimization models and the relevant solution algorithms.This study focuses on the reactive scheduling of EOSs under cloud uncertainties.First,using an example,we describe the reactive scheduling problem in detail,clarifying its significance and key issues.Considering the two key objectives of observation profits and scheduling stability,we construct a multi-objective optimization mathematical model.Then,we obtain the possible disruptions of EOS scheduling during execution under cloud uncertainties,adopting an event-driven policy for the reactive scheduling.For the different disruptions,different reactive scheduling algorithms are designed.Finally,numerous simulation experiments are conducted to verify the feasibility and effectiveness of the proposed reactive scheduling algorithms.The experimental results show that the reactive scheduling algorithms can both improve observation profits and reduce system perturbations.

Digital earth Australia-unlocking new value from earth observation data

Petascale archives of Earth observations from space(EOS)have the potential to characterise water resources at continental scales.For this data to be useful,it needs to be organised,converted from individual scenes as acquired by multiple sensors,converted into“analysis ready data”,and made available through high performance computing platforms.Moreover,converting this data into insights requires integration of non-EOS data-sets that can provide biophysical and climatic context for EOS.Digital Earth Australia has demonstrated its ability to link EOS to rainfall and stream gauge data to provide insight into surface water dynamics during the hydrological extremes of flood and drought.This information is supporting the characterisation of groundwater resources across Australia’s north and could potentially be used to gain an understanding of the vulnerability of transport infrastructure to floods in remote,sparsely gauged regions of northern and central Australia.

Rapid,high-resolution detection of environmental change over continental scales from satellite data–the Earth Observation Data Cube

The effort and cost required to convert satellite Earth Observation(EO)data into meaningful geophysical variables has prevented the systematic analysis of all available observations.To overcome these problems,we utilise an integrated High Performance Computing and Data environment to rapidly process,restructure and analyse the Australian Landsat data archive.In this approach,the EO data are assigned to a common grid framework that spans the full geospatial and temporal extent of the observations–the EO Data Cube.This approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement.We demonstrate the utility of the approach with rapid time-series mapping of surface water across the entire Australian continent using 27 years of continuous,25 m resolution observations.Our preliminary analysis of the Landsat archive shows how the EO Data Cube can effectively liberate high-resolution EO data from their complex sensor-specific data structures and revolutionise our ability to measure environmental change.

Earth observation satellite data receiving, processing system and data sharing

The China Remote Sensing Satellite Ground Station was established in 1986.It currently has three receiving stations in the north,west,and south of China,with the capacity to receive data from 15 international and domestic Earth observation satellites covering the entire Chinese territory and 70%of Asia.Meanwhile,a systematic,integrated,and standardized spatial information service system has been built.A data-sharing project for medium-resolution Earth observation satellites has been conducted and plays an important role in land,ocean,and atmospheric resource investigation and environmental monitoring.

The digital Earth Observation Librarian:a data mining approach for large satellite images archives

Throughout the years,various Earth Observation(EO)satellites have generated huge amounts of data.The extraction of latent information in the data repositories is not a trivial task.New methodologies and tools,being capable of handling the size,complexity and variety of data,are required.Data scientists require support for the data manipulation,labeling and information extraction processes.This paper presents our Earth Observation Image Librarian(EOLib),a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data,in general.The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments(PGS).EOLib is composed of several modules which offer functionalities such as data ingestion,feature extraction from SAR(Synthetic Aperture Radar)data,meta-data extraction,semantic definition of the image content through machine learning and data mining methods,advanced querying of the image archives based on content,meta-data and semantic categories,as well as 3-D visualization of the processed images.EOLib is operated by DLR’s(German Aerospace Center’s)Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen,Germany.

Publishing China satellite data on the GEOSS Platform

This paper is the first of a series that describes some of the main dataset resources presently shared through the GEOSS Platform.The GEOSS Platform has been created to provide the technological tool to implement the Global Earth Observation System of Systems(GEOSS);it is a brokering infrastructure that presently brokers more than 190 autonomous data catalogs and information systems.The paper analyses the China Satellite datasets and describes the data publishing process from China GEOSS Data Provider to the GEOSS Platform considering both administrative registration as well as the technical registration.The China Satellite datasets are considered as one of the most important satellite data shared by the GEOSS Platform.The analysis provides some insights as well about GEOSS user searches for China Satellite datasets.